Analysis of Variance (ANOVA) is a hypothesis testing procedure that tests whether two or more means are significantly different from each other.

This article describes how to go create an ANOVA Table as shown below. The image below shows the results for a linear regression using Gender, Age, and Coke's perception of weight-conscious to predict the perception of Coke as a feminine brand.

## Requirements

- Familiarity with the
*Structure*and*Value Attributes*of Variable Sets. - A data set consisting of at least two variables.

## Method

- In the
**Anything**menu select**Advanced Analysis > Analysis of Variance > ANOVA**. - In the
**object inspector**go to the**Inputs**tab. - In the
**Outcome**dropdown select the variable to be predicted by the*predictor variables.* - Select the predictor variable(s) from the
**Predictor(s)**list. - Specify the type of regression to perform in the
**Inputs > Type**.**Linear**- The linear regression option is most commonly used when the dependent variable is continuous.**Binary Logit**- This is a form of regression analysis that models a binary dependent variable (e.g. yes/no, pass/fail, win/lose).**Ordered Logit**- The*Ordered Logit*is a form of regression analysis that models a discrete and ordinal dependent variable with more than two outcomes (Net promoter Score, Customer Satisfaction rating, etc.).**Multinomial Logit**- The Multinomial Logit is a form of regression analysis that models a discrete and nominal dependent variable with more than two outcomes (Yes/No/Maybe, Red/Green/Blue, Brand A/Brand B/Brand C, etc.).**Quasi-Poisson**- The*Quasi-Poisson Regression*is a generalization of the Poisson regression and is used when modeling an overdispersed count variable.**NBD**- The*Negative Binomial Distribution*(NBD) Regression is a generalization of the Poisson regression, in which the Negative Binomial distribution replaces the Poisson distribution.

- OPTIONAL: Select the desired Missing Data treatment. (See Missing Data Options).
- OPTIONAL: Select the
**Auxiliary variables**Variables to be used when imputing missing values (in addition to all the other variables in the model). Only shown when**Missing data**is set to**Multiple imputation**. - OPTIONAL: Select the desired
**Output**type.**ANOVA**The ANOVA table as shown in the example above.**Summary**The regression coefficients, their standard errors, t-statistics and p-values.**Detail**The R output from the regression fitting.

- OPTIONAL: Select
**Variable names**to display variable names in the output instead of labels. - OPTIONAL: To compute standard errors that are robust to violations of the assumption of constant variance (i.e., heteroscedasticity) select
**Robust standard errors.**This is only available when**Type**is set to**Linear**. See Robust Standard Errors for more information.

## Next

How to Create a One-Way ANOVA table

How to Create a One-Way MANOVA table

How to Create a Table of Means